A MODEL OF EMPLOYEE PARTICIPATION IN TELECOMMUTING PROGRAMS BASED ON STATED PREFERENCE DATA (CHAPTER 10 OF TRAVEL BEHAVIOUR RESEARCH: UPDATING THE STATE OF PLAY)

Telecommuting in its various forms has been advocated as a travel demand management strategy, with potential to reduce congestion, energy consumption and air pollution. It also affords certain segments of society the opportunity to participate in the work force, improving their quality of life and offering employers an expanded pool of qualified workers. Several other productivity and employee-morale benefits have also been attributed to telecommuting. A key factor in the effectiveness of telecommuting is the extent to which it is adopted by employers and empolyees alike. Recognising that two principal actors are involved in telecommuting adoption, this chapter is intended to formulate the employee adoption process, assuming such programs are provided by the employer in the organisation. On the basis of stated preference data obtained from a survey in three Texas cities in the USA, an employee telecommuting choice model is developed. The model is formulated and estimated under the framework of the dynamic generalised ordinal probit model developed by the authors, which is based on the ordered-response theory but advances existing ordinal probit models by allowing the specification of stochastic utility thresholds and autocorrelation among observations. Estimation results confirm that the employee adoption process is affected by his/her attitudes toward telecommuting and the program design, defined on the basis of who assumes additional telecommuting costs, and the corresponding salary changes for the telecommuter. The employee's choice of telecommuting is also influenced by his/her personal, household and job characteristics as well as commuting attributes. Another important feature that emerges from the estimation results is that the dynamic structure of the generalised ordinal probit model successfully captures the autocorrelation among responses from the same employee, which ultimately improves the precision of the parameter estimates.